1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
30/6/2023 Electricidad 49877 Andrés enel
30/6/2023 Agua 12706 Andrés NA
1/7/2023 Comida 48400 Andrés de la ostia
2/7/2023 Comida 70135 Tami Supermercado
2/7/2023 Parafina 19418 Tami NA
4/7/2023 Comida 12000 Andrés nueces y almendras 500 gr
6/7/2023 Gas 68950 Andrés lipigas
8/7/2023 Agua 12706 Andrés NA
8/7/2023 Comida 57693 Tami Supermercado
9/7/2023 correo 8000 Andrés correos de chile raul miranda
9/7/2023 mouse 51980 Andrés NA
9/7/2023 lamina 13800 Andrés NA
12/7/2023 Comida 26780 Andrés NA
12/7/2023 Netflix 11880 Tami Netflix junio y julio 2023
17/7/2023 Comida 86974 Tami Supermercado
19/7/2023 VTR 21990 Andrés NA
25/7/2023 Comida 75171 Tami Supermercado
24/7/2023 Enceres 27065 Andrés secador platos
30/7/2023 Comida 19630 Andrés choritos, costa rama y weas
30/7/2023 Parafina 19920 Tami NA
30/7/2023 Electricidad 49345 Andrés PAC ENEL 01686518
31/7/2023 Comida 78380 Tami Supermercado
3/8/2023 Comida 19000 Andrés NA
3/8/2023 Diosi 15980 Andrés NA
6/8/2023 Gas 16650 Andrés NA
6/8/2023 Gas 23666 Tami Parafina
8/8/2023 Comida 78577 Tami Supermercado
9/8/2023 Agua 11520 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 6.9346e+08   2    7.2402  8e-04 ***
## lag_depvar    8.4623e+10   1 1767.0458 <2e-16 ***
## Residuals     2.8973e+10 605                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838  1116.079 13341.60 0.0155225
## 2-0 28550.140 22979.938 34120.34 0.0000000
## 2-1 21321.302 18018.714 24623.89 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
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## 26   17692.29             0   24642.71
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## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   453 50784.40 15033.613
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1960.63935   4015.41263   -513.26126   2459.58014  -2920.57021    536.57855 
##            8            9           10           11           12           13 
##  -5629.62927  -1217.30036  -3998.69989   -481.54500  -4994.20958  -1702.06489 
##           14           15           16           17           18           19 
##   -991.79402    293.22082  -3307.28882   -461.90316  -2201.99916   6524.94566 
##           20           21           22           23           24           25 
##  -1525.87635  -1215.68726   1462.13681  -1178.04218    235.33943   1703.32224 
##           26           27           28           29           30           31 
##  -7072.16035    906.70289   8171.80907    489.22589     57.79523  -2332.53706 
##           32           33           34           35           36           37 
##   1616.63990   4629.36821   1228.70275   2497.36543  -1745.56884   4701.35269 
##           38           39           40           41           42           43 
##   4358.85941  -2182.89990  -2923.05278  -1089.04741 -10733.86923   7186.92726 
##           44           45           46           47           48           49 
##   2542.47902   1380.44253   8131.42897    792.18726   6628.88093   6868.91300 
##           50           51           52           53           54           55 
##  -5678.38362  -4676.69650  -5004.36948  -7931.29271   6047.07951  -4085.98647 
##           56           57           58           59           60           61 
##  -4943.62859   3763.27204    846.68995    -58.59603    119.18539  -5014.68670 
##           62           63           64           65           66           67 
##  18060.33980   3767.79602  -3497.42675   6018.63542   7486.47622  14838.77832 
##           68           69           70           71           72           73 
##   2017.70278 -12911.27707  -1176.72196   4743.88805  -4764.69552  -4335.38473 
##           74           75           76           77           78           79 
## -10481.27161   2374.86453  -5454.35557    961.79232  -6943.93275    409.86941 
##           80           81           82           83           84           85 
##  -2468.16478  -2816.44840  -4065.75726   -693.84301   2173.14308   3663.09004 
##           86           87           88           89           90           91 
##    428.54007   -521.54780    159.77597   4272.16916  -1144.90914   1155.29565 
##           92           93           94           95           96           97 
##  -2048.45491  -1050.81323    161.73530    263.18947  -7490.92919   2310.31043 
##           98           99          100          101          102          103 
##  -8648.86394  -3067.70188  -4179.64737  -1899.42041  -1420.35337   3029.93373 
##          104          105          106          107          108          109 
##  -2441.18965   2484.20725  -1227.55006    898.81470   2534.82529  -3173.35705 
##          110          111          112          113          114          115 
##  -4771.09740   -939.57221   1817.41130  11638.11395  -1172.04014   2718.07091 
##          116          117          118          119          120          121 
##   4333.69252   3608.33694   -971.55873  -4614.74106  -3682.56606   2318.88492 
##          122          123          124          125          126          127 
##  -1709.34653   1343.78733   8875.32886    952.37769    231.57897  -2431.02333 
##          128          129          130          131          132          133 
##   2708.91519   7127.03885   1149.59617  -8368.75364   1777.72156   4178.68214 
##          134          135          136          137          138          139 
##  -3083.79727  -1381.25141   -834.22534  -3870.88044   1152.22505   -509.89122 
##          140          141          142          143          144          145 
##  -2930.70104   1674.21411  -1901.56067  -7865.75751   1928.76692  -3555.27814 
##          146          147          148          149          150          151 
##   2001.40464   -323.83329    962.86789   -401.07295   1312.48537   1166.06819 
##          152          153          154          155          156          157 
##   3351.06643  -4832.15402  -1197.37921  -3267.26155   5896.91810   9755.19767 
##          158          159          160          161          162          163 
##  -3425.92573  -4791.51447   3560.53572    209.58726   2727.26990  -5840.20473 
##          164          165          166          167          168          169 
##  -6729.52068   4120.53277  17418.35533   3833.23764   -169.24525  -2235.03184 
##          170          171          172          173          174          175 
##   -929.13740   3747.18693    -41.89818  -7900.48613   2952.17027   4444.95739 
##          176          177          178          179          180          181 
##    786.18518   8910.97608  -9009.33463  -3338.57431 -10647.91741 -11245.91496 
##          182          183          184          185          186          187 
##   1134.83369   9232.05700  -1379.59544   5972.50872   6663.65629  13327.37634 
##          188          189          190          191          192          193 
##   8714.05638  -3726.77174   2728.48959  10630.82023  -1308.62471  -2159.22752 
##          194          195          196          197          198          199 
## -10045.66241  -6245.29797   1287.31482  -5163.69142  -9772.90815   5324.07740 
##          200          201          202          203          204          205 
##  -3056.97398  -1718.89665   -813.39650   6490.96925   9949.69019    737.25065 
##          206          207          208          209          210          211 
##   3077.87449   3268.05214   5970.88814  13059.26352  -5362.93723 -11055.79921 
##          212          213          214          215          216          217 
##  -5550.18869 -10526.83716  -5107.82854   1464.24293 -13038.25970  16258.40773 
##          218          219          220          221          222          223 
##   7857.62281   1652.26618  26820.73345  12893.13441   7776.25197  14485.73770 
##          224          225          226          227          228          229 
##  -3378.25212  -1301.90419   4153.04741    731.51713   3083.70366   9335.27306 
##          230          231          232          233          234          235 
##   6213.68554  -1504.97437  -1483.38928   9721.24551 -11152.86229  -7068.06596 
##          236          237          238          239          240          241 
##  -8409.15354 -10053.49773   3038.38466   1358.29744  -8267.17030  -9027.27025 
##          242          243          244          245          246          247 
##   8992.06306  -7759.51795   2433.63892 -10313.14679  -4146.17768   1317.57059 
##          248          249          250          251          252          253 
##    936.30804 -12353.27497   3510.69425   1991.54365   4179.80178   2155.65218 
##          254          255          256          257          258          259 
##  -1112.69323  11180.68882  21025.99025   3515.49802  -3959.07968   4350.68260 
##          260          261          262          263          264          265 
##  -1440.48807   3951.29196  -4624.16313 -10730.20928  -4674.03427   -506.72774 
##          266          267          268          269          270          271 
##  -5170.47560   8756.93195  -4213.64735   4217.86455  -2037.84441   4480.22395 
##          272          273          274          275          276          277 
##    797.84256   7393.46817  -1263.86712  12147.92654  -4369.77521   1876.97128 
##          278          279          280          281          282          283 
##   -220.53635   7985.67293  -4868.93812  -2603.94432 -11166.48194  -2672.54428 
##          284          285          286          287          288          289 
##  18638.67625   7932.26506   2933.59031   -427.36611   1081.04581   6563.33218 
##          290          291          292          293          294          295 
##   7083.87527 -18535.74069 -11078.18079  -8148.16568   9588.03871   3094.67706 
##          296          297          298          299          300          301 
##  -1124.68086  27449.46818  10333.32370   5219.60790   9839.51873   3217.20845 
##          302          303          304          305          306          307 
##   -690.78460   8191.19449 -23970.33395  -3433.69558   -103.76766  -6895.74305 
##          308          309          310          311          312          313 
##  -3950.72545   2933.05766  -9155.16404  -3254.63397  -8216.78239   1492.33060 
##          314          315          316          317          318          319 
##  -3186.46911   2008.91126  -4086.42262  27424.73496   -540.67504   3452.26696 
##          320          321          322          323          324          325 
##  11002.79244   5828.15148  32637.58431   5587.49057 -20476.55444   2043.27091 
##          326          327          328          329          330          331 
##   1351.37298  -6237.70376  -1574.89215 -33130.86561    801.58881  -2341.05802 
##          332          333          334          335          336          337 
##   -119.89216  -3168.11922   4085.29431   -385.10982  -6888.81769  -3089.50234 
##          338          339          340          341          342          343 
##  -2167.81798  -7651.62128   3843.69149  -1327.93535  -1687.97241   -940.92335 
##          344          345          346          347          348          349 
##    237.61628    557.62289  -1528.02766  -9359.07059 -13181.02230   2262.65773 
##          350          351          352          353          354          355 
##  -4325.36744  -3668.55337  -5991.81771   1720.90517   1392.68989   2790.60556 
##          356          357          358          359          360          361 
##  -3695.63438   -462.41280    739.56094   7091.01578    409.73437     96.23386 
##          362          363          364          365          366          367 
##   2716.56420  -2599.59014   -747.34295  -8617.60201  -4555.01037  -6159.92810 
##          368          369          370          371          372          373 
##  -4923.00362  -7239.67623   5000.79153    413.33219   7179.24918  -7518.08250 
##          374          375          376          377          378          379 
##  -2190.76628  -3318.61634  -2407.32299 -12399.66967   1893.07740 -10607.16742 
##          380          381          382          383          384          385 
##   5671.71466   9374.19553   3239.56072  -2262.13363   1724.96800   6876.37623 
##          386          387          388          389          390          391 
##  11585.13746  -5564.49255  -5185.12925    -26.46093   8689.62684   1992.62521 
##          392          393          394          395          396          397 
##  11398.73608  -9643.36429   2921.52466    868.77948    712.84090   -508.99751 
##          398          399          400          401          402          403 
##   -430.71531 -14364.56647   8555.73235  -1077.17669  -1272.32223   7077.76724 
##          404          405          406          407          408          409 
##  -7788.33524  -1203.64896  -2438.15643  -5732.30997  -2796.95826  -3857.30542 
##          410          411          412          413          414          415 
##  -8703.90398   6147.80390   1713.93372  -7278.35432  -7633.69990  14247.29657 
##          416          417          418          419          420          421 
##   3945.02299   4641.79252  -7864.25271  -4627.44688  -2507.98225   2906.80607 
##          422          423          424          425          426          427 
## -13896.73970  -2756.59855  -9061.29002   3014.24955   7020.79989   6677.20540 
##          428          429          430          431          432          433 
##  -3839.99283  -3997.66762  -4619.44434  -1707.33273  -5628.92607  -6567.20731 
##          434          435          436          437          438          439 
##  -5915.60610  -1376.57713   -817.73823  -4930.53264   2613.04629   4905.79822 
##          440          441          442          443          444          445 
##  -4947.55690  -2073.13382   1659.26646  -3733.23228   2923.98731  -6461.79387 
##          446          447          448          449          450          451 
## -12028.47198  -4492.78932   9657.91521  -1935.45157   4848.84570  -5734.30748 
##          452          453          454          455          456          457 
##  -1019.38285    490.04602   3144.38979 -12123.73219   3439.21329  -6594.80689 
##          458          459          460          461          462          463 
##   6595.32028   3141.35073   2661.01516  -3673.98906   2239.29849    155.62911 
##          464          465          466          467          468          469 
##   1956.36854   -346.11046   3520.62207  -2448.51076   5975.65115  -6734.57116 
##          470          471          472          473          474          475 
##  -2805.50832  -2061.94757  -4528.46768   3109.11589   7939.34318  -5819.83682 
##          476          477          478          479          480          481 
##   1640.22262  -6009.52103  -2712.83836   2133.02568 -12786.54469  -9690.83576 
##          482          483          484          485          486          487 
##  -1184.98517     53.90802   -906.64441  -1274.10110  -9509.07122  11123.00377 
##          488          489          490          491          492          493 
##   6358.62685   7592.81367  -5214.73946   5547.01095   9507.45889   6323.14933 
##          494          495          496          497          498          499 
## -13178.99552 -10376.71741  -3321.82345   -999.06994   -412.91284  -7506.72078 
##          500          501          502          503          504          505 
##    690.66766   4387.93123   5650.49204    845.37671    270.27952  -7049.03618 
##          506          507          508          509          510          511 
##    712.01061  -4895.28478   1957.74062  -1149.35291  -8012.95766   -504.74903 
##          512          513          514          515          516          517 
##  -2567.36151   -486.09781   1443.03089  -9363.79221  -7688.81803  24327.28190 
##          518          519          520          521          522          523 
##  10049.35628   6162.57954  -5028.72838   3050.93403  17279.27502  11833.74940 
##          524          525          526          527          528          529 
## -23740.95839  -4846.51015  -3558.72004   4722.64671   -172.06419 -10921.68264 
##          530          531          532          533          534          535 
##   4498.54382  14056.62236  -4733.44110   4573.42662   5779.27082  -1537.64016 
##          536          537          538          539          540          541 
##  -4312.21500  -6883.33981  -1958.11549   8457.10863    323.85948  -7946.52107 
##          542          543          544          545          546          547 
##   1955.03470   -441.92263    523.73950 -10867.11685 -10967.95924   2074.55789 
##          548          549          550          551          552          553 
##   7073.47172  -1183.91381    969.68446  -7574.36447   8666.03478   1077.40243 
##          554          555          556          557          558          559 
## -11765.43171   9262.26396   8830.17738    329.17769   5075.63580  -3327.01427 
##          560          561          562          563          564          565 
##  14321.09392  21798.12158  -6045.58160  -9340.64399   7025.62178    509.18215 
##          566          567          568          569          570          571 
##   3722.02945  -7101.63050 -17095.72061   6757.61380   6588.15810   2103.76194 
##          572          573          574          575          576          577 
##   3310.35537   1998.99432  -1930.30839  14930.36085  -9342.89537  -6022.08756 
##          578          579          580          581          582          583 
##   8885.98444   3092.05916  -6300.64478   7700.98252  -3559.01118  -2568.43502 
##          584          585          586          587          588          589 
##  15888.17226 -14209.39823   8604.75406    304.52644  -5984.69386   -573.31015 
##          590          591          592          593          594          595 
##    429.67611 -10471.38585   1909.51608  -7007.13090   3166.09013   9000.15030 
##          596          597          598          599          600          601 
##  -7298.18727   6002.32102   2931.57589   7070.38568  -2932.57334   6376.41027 
##          602          603          604          605          606          607 
##  -8034.75147   2453.39223   1483.36944   3358.10100   1733.18605    639.91846 
##          608          609          610 
##  -5571.46746   8269.10752   -933.37608 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17308.65 20123.59 24329.40 24050.56 26377.28 23740.14 24448.34 19734.44 
##       10       11       12       13       14       15       16       17 
## 19473.99 16846.83 17615.50 14381.92 14432.51 15089.64 16767.00 15106.05 
##       18       19       20       21       22       23       24       25 
## 16129.00 15509.63 22511.88 21606.26 21092.01 22960.61 22294.23 22939.39 
##       26       27       28       29       30       31       32       33 
## 24764.45 18761.58 20468.19 28216.77 28273.78 27950.39 25606.65 26993.20 
##       34       35       36       37       38       39       40       41 
## 30792.73 31137.21 32530.43 30069.22 34084.14 37255.90 34345.34 31192.33 
##       42       43       44       45       46       47       48       49 
## 30053.15 20739.36 28172.95 30581.84 31658.71 38419.38 37919.69 42529.09 
##       50       51       52       53       54       55       56       57 
## 46717.38 39497.98 34127.94 29207.01 22429.06 28647.84 25267.20 21606.73 
##       58       59       60       61       62       63       64       65 
## 25965.17 27210.45 27504.10 27911.26 23828.95 40232.35 42055.43 37355.22 
##       66       67       68       69       70       71       72       73 
## 41514.52 46374.51 56921.87 54958.13 40368.44 37902.54 40886.27 35250.96 
##       74       75       76       77       78       79       80       81 
## 30754.70 21563.42 24728.64 20700.49 22762.93 17716.27 19708.88 18944.16 
##       82       83       84       85       86       87       88       89 
## 17982.90 16073.70 17337.00 20904.20 25271.89 26250.55 26275.22 26884.97 
##       90       91       92       93       94       95       96       97 
## 30963.34 29807.13 30795.17 28881.53 28090.41 28454.38 28856.36 22506.55 
##       98       99      100      101      102      103      104      105 
## 25487.44 18596.84 17465.93 15528.85 15825.21 16494.92 20916.90 20010.79 
##      106      107      108      109      110      111      112      113 
## 23482.12 23274.47 24931.60 27775.79 25302.24 21786.00 22058.30 24674.60 
##      114      115      116      117      118      119      120      121 
## 35416.04 33629.36 35446.02 38410.38 40344.13 38058.74 32938.42 29321.26 
##      122      123      124      125      126      127      128      129 
## 31380.49 29679.93 30848.10 38361.77 38008.28 37080.45 33979.51 35740.53 
##      130      131      132      133      134      135      136      137 
## 41077.26 40523.90 31825.28 33075.75 36229.37 32680.68 31086.23 30181.59 
##      138      139      140      141      142      143      144      145 
## 26777.63 28176.03 27948.27 25660.79 27662.27 26302.61 19977.23 22973.42 
##      146      147      148      149      150      151      152      153 
## 20824.74 23768.12 24301.99 25874.36 26054.37 27689.79 28975.79 31973.58 
##      154      155      156      157      158      159      160      161 
## 27495.09 26766.40 24349.37 30176.66 41446.35 39795.51 37190.32 42153.70 
##      162      163      164      165      166      167      168      169 
## 43546.30 46923.49 42440.81 37801.18 43164.93 59282.33 61469.39 59901.46 
##      170      171      172      173      174      175      176      177 
## 56763.14 55180.53 57852.47 56887.63 49267.12 52058.61 55758.81 55794.60 
##      178      179      180      181      182      183      184      185 
## 62842.62 53452.57 50240.35 41153.20 32788.45 36256.94 46245.88 45708.06 
##      186      187      188      189      190      191      192      193 
## 51593.34 57273.20 67933.94 73156.91 66923.08 67114.32 74104.48 69829.94 
##      194      195      196      197      198      199      200      201 
## 65403.52 54769.30 48867.11 50275.26 45919.91 38177.49 44529.40 42776.90 
##      202      203      204      205      206      207      208      209 
## 42418.97 42891.89 49608.88 58397.32 58031.13 59736.38 61373.40 65121.59 
##      210      211      212      213      214      215      216      217 
## 74480.79 66653.37 54976.33 49646.27 40744.69 37736.90 40815.26 30948.59 
##      218      219      220      221      222      223      224      225 
## 47729.66 54967.45 55859.12 78366.44 85776.46 87756.98 95262.25 86315.76 
##      226      227      228      229      230      231      232      233 
## 80382.24 79968.91 76656.87 75827.87 80511.17 81859.97 76358.53 71625.75 
##      234      235      236      237      238      239      240      241 
## 77215.29 64014.49 56141.30 48183.21 39889.90 44034.27 46162.60 39687.56 
##      242      243      244      245      246      247      248      249 
## 33438.79 43604.66 37916.79 41807.86 34159.46 32880.00 36493.83 39285.70 
##      250      251      252      253      254      255      256      257 
## 30219.16 36089.88 39848.20 44984.06 47671.55 47169.88 57354.01 74652.79 
##      258      259      260      261      262      263      264      265 
## 74469.94 67856.46 69321.49 65585.14 67014.88 60843.35 50239.61 46312.01 
##      266      267      268      269      270      271      272      273 
## 46519.05 42669.93 51374.22 47689.56 51789.27 49927.20 53948.44 54241.10 
##      274      275      276      277      278      279      280      281 
## 60190.30 57851.36 67414.63 61408.31 61615.96 59983.76 65661.51 59463.09 
##      282      283      284      285      286      287      288      289 
## 56065.91 45736.69 44151.61 61188.45 66655.84 67060.65 64507.53 63605.24 
##      290      291      292      293      294      295      296      297 
## 67560.84 71426.74 52638.75 42853.02 36931.96 47136.32 50341.40 49465.39 
##      298      299      300      301      302      303      304      305 
## 73387.39 79265.39 79925.48 84485.65 82704.64 77791.23 81218.76 56402.12 
##      306      307      308      309      310      311      312      313 
## 52705.62 52389.03 46249.58 43490.66 47053.16 39689.78 38426.35 33049.53 
##      314      315      316      317      318      319      320      321 
## 36791.18 35981.80 39769.85 37777.12 63271.25 61136.88 62742.06 70649.56 
##      322      323      324      325      326      327      328      329 
## 73009.84 98202.80 96598.84 72702.87 71514.34 69890.28 61933.18 59088.01 
##      330      331      332      333      334      335      336      337 
## 29376.84 33022.63 33457.18 35750.83 35099.13 40800.82 41864.25 37165.65 
##      338      339      340      341      342      343      344      345 
## 36388.96 36514.19 31886.17 37817.22 38473.12 38728.64 39594.53 41360.23 
##      346      347      348      349      350      351      352      353 
## 43161.60 42916.07 35940.59 26615.20 31899.37 30773.27 30367.96 28011.38 
##      354      355      356      357      358      359      360      361 
## 32637.31 36349.11 40762.21 38971.70 40217.72 42331.98 49643.55 50187.91 
##      362      363      364      365      366      367      368      369 
## 50387.29 52822.59 50334.49 49785.32 42513.72 39742.21 35962.43 33766.25 
##      370      371      372      373      374      375      376      377 
## 29868.64 37074.10 39335.18 47131.51 41171.34 40624.76 39178.61 38716.67 
##      378      379      380      381      382      383      384      385 
## 29687.64 34233.74 27364.00 35490.38 45706.58 49231.71 47524.60 49493.77 
##      386      387      388      389      390      391      392      393 
## 55643.58 65021.78 58309.84 52840.60 52572.37 59868.52 60385.98 68956.65 
##      394      395      396      397      398      399      400      401 
## 58185.48 59734.65 59299.73 58789.43 57293.43 56069.00 42977.27 51465.89 
##      402      403      404      405      406      407      408      409 
## 50477.61 49455.52 55784.48 48411.22 47730.16 46075.74 41801.82 40645.73 
##      410      411      412      413      414      415      416      417 
## 38731.48 32892.34 40676.21 43569.50 38301.99 33445.70 48149.41 51950.78 
##      418      419      420      421      422      423      424      425 
## 55835.68 48389.88 44754.70 43445.62 46991.60 35541.46 35273.72 29597.32 
##      426      427      428      429      430      431      432      433 
## 35124.06 43357.65 50171.99 46973.95 44075.73 41035.62 40925.07 37442.64 
##      434      435      436      437      438      439      440      441 
## 33624.61 30889.86 32448.17 34276.68 32303.81 37115.06 43250.56 40039.56 
##      442      443      444      445      446      447      448      449 
## 39748.88 42721.38 40631.30 44575.79 39876.33 31009.79 29860.37 41089.17 
##      450      451      452      453      454      455      456      457 
## 40774.30 46361.74 42047.10 42392.81 43995.04 47671.30 37659.79 42454.38 
##      458      459      460      461      462      463      464      465 
## 37929.25 45412.93 48893.27 51484.27 48250.70 50565.09 50764.35 52491.68 
##      466      467      468      469      470      471      472      473 
## 51994.95 54905.51 52263.92 57258.14 50594.08 48231.95 46834.04 43496.46 
##      474      475      476      477      478      479      480      481 
## 47210.23 54589.41 49079.21 50763.24 45610.84 44008.12 46809.12 36342.69 
##      482      483      484      485      486      487      488      489 
## 29976.84 31825.09 34491.36 35964.53 36919.50 30632.00 43020.94 49606.04 
##      490      491      492      493      494      495      496      497 
## 56359.31 51130.42 55908.97 63456.56 67225.00 53636.29 44320.39 42367.64 
##      498      499      500      501      502      503      504      505 
## 42687.20 43469.44 38018.33 40390.21 45631.94 51249.48 51951.15 52060.46 
##      506      507      508      509      510      511      512      513 
## 45833.42 47158.28 43459.69 46184.07 45853.53 39640.18 40758.50 39942.95 
##      514      515      516      517      518      519      520      521 
## 41036.11 43646.36 36567.25 31899.86 55520.07 63588.71 67200.44 60654.21 
##      522      523      524      525      526      527      528      529 
## 61978.58 75410.96 82308.96 57541.80 52469.72 49201.35 53530.92 53042.83 
##      530      531      532      533      534      535      536      537 
## 43337.17 48272.66 60790.30 55373.00 58732.30 62675.07 59760.93 54847.77 
##      538      539      540      541      542      543      544      545 
## 48383.83 47054.89 54902.43 54655.66 47299.68 49498.21 49326.83 50012.83 
##      546      547      548      549      550      551      552      553 
## 40767.39 32695.30 36988.10 45013.06 44812.32 46498.94 40576.39 49487.60 
##      554      555      556      557      558      559      560      561 
## 50629.86 40524.45 49957.68 57731.68 57103.79 60660.87 56475.91 68103.59 
##      562      563      564      565      566      567      568      569 
## 84603.72 74806.64 63499.38 67868.67 66014.26 67187.49 58852.72 43022.67 
##      570      571      572      573      574      575      576      577 
## 49952.13 55790.52 56959.93 59012.01 59651.74 56810.64 68918.90 58412.37 
##      578      579      580      581      582      583      584      585 
## 52206.30 59721.94 61208.93 54381.02 60576.73 56202.86 53280.83 66697.54 
##      586      587      588      589      590      591      592      593 
## 52290.82 59552.04 58654.69 52447.88 51760.90 52033.81 42854.63 45619.85 
##      594      595      596      597      598      599      600      601 
## 40307.05 44504.85 53169.04 46575.68 52368.42 54719.33 60324.29 56525.88 
##      602      603      604      605      606      607      608      609 
## 61285.18 52949.18 54807.92 55575.47 57857.53 58425.08 57971.04 52214.32 
##      610 
## 59196.09 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8349
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    7.240221  0.5528543    3.540297
## t2* 1767.045771 23.6324382  221.809740
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    2.801575       7.365043   14.23749
## 2    lag_depvar 1449.726721    1778.726325 2174.15546

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Aug 14 01:04:11 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 14 01:04:22 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 14 01:04:33 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 14 01:04:44 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 14 01:04:55 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 14 01:05:05 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 14 01:05:16 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 14 01:05:27 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 14 01:05:38 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 14 01:05:49 2023
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 3.630286 5.410333 5.629750 6.5064884
Comida 351.597429 310.278417 314.087500 342.1973488
Comunicaciones 0.000000 0.000000 0.000000 0.0000000
Electricidad 39.311429 47.072333 38.297667 33.5250930
Enceres 26.902429 20.086417 17.443792 25.0026047
Farmacia 2.854286 1.831667 7.913875 8.7989302
Gas/Bencina 43.473714 44.325000 28.954333 28.0539535
Diosi 11.984286 31.180667 41.934250 35.7155349
donaciones/regalos 0.000000 0.000000 7.170083 6.3888140
Electrodomésticos/ Mantención casa 0.000000 3.944000 30.269500 19.2899535
VTR 12.567143 25.156667 22.121792 19.7269302
Netflix 5.771429 7.151583 7.090167 7.1983953
Otros 0.000000 3.151083 1.575542 0.8793721
Total 498.092429 499.588167 522.488250 533.2834186
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2078, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-09-09 00:04:58 sería de: 36.613 pesos// Percentil 95% más alto proyectado: 39.771,07

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36179.82 36177.46
Lo.80 36191.63 36187.51
Point.Forecast 36612.72 37560.89
Hi.80 38384.88 42320.21
Hi.95 39357.46 44839.64


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2450  1007.9316
## s.e.  0.1372    29.9385
## 
## sigma^2 = 28818:  log likelihood = -352.89
## AIC=711.78   AICc=712.26   BIC=717.75
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.2217   714.6894   9.5292
## s.e.  0.1389   312.3797  10.0966
## 
## sigma^2 = 28928:  log likelihood = -352.46
## AIC=712.93   AICc=713.74   BIC=720.88
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 747.9152 664.7484 710.2107
Lo.80 866.2449 783.5362 794.5381
Point.Forecast 1089.7748 1007.9316 982.1172
Hi.80 1313.3048 1232.3271 1272.7341
Hi.95 1431.6344 1351.1148 1459.9263


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.7      
##  [7] tidytext_0.4.1      DT_0.28             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.2       xts_0.13.1         
## [13] forecast_8.21       wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.1     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.3       lubridate_1.9.2     forcats_1.0.0      
## [22] dplyr_1.1.2         purrr_1.0.1         tidyr_1.3.0        
## [25] tibble_3.2.1        ggplot2_3.4.2       tidyverse_2.0.0    
## [28] sjPlot_2.8.14       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.8.2      httr_1.4.6         
## [34] readxl_1.4.3        zoo_1.8-12          stringr_1.5.0      
## [37] stringi_1.7.12      data.table_1.14.8   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.8          readr_2.1.4        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       lazyeval_0.2.2      splines_4.1.2      
##   [7] crosstalk_1.2.0     digest_0.6.31       htmltools_0.5.5    
##  [10] fansi_1.0.4         ggfortify_0.4.16    magrittr_2.0.3     
##  [13] tzdb_0.4.0          modelr_0.1.11       vroom_1.6.3        
##  [16] timechange_0.2.0    anytime_0.3.9       tseries_0.10-54    
##  [19] colorspace_2.1-0    xfun_0.39           crayon_1.5.2       
##  [22] jsonlite_1.8.4      lme4_1.1-34         glue_1.6.2         
##  [25] gtable_0.3.3        emmeans_1.8.7       sjstats_0.18.2     
##  [28] sjmisc_2.8.9        car_3.1-2           quantmod_0.4.24    
##  [31] abind_1.4-5         mvtnorm_1.2-2       DBI_1.1.3          
##  [34] ggeffects_1.2.3     Rcpp_1.0.10         viridisLite_0.4.2  
##  [37] xtable_1.8-4        performance_0.10.4  bit_4.0.5          
##  [40] htmlwidgets_1.6.2   timeSeries_4030.106 gplots_3.1.3       
##  [43] ellipsis_0.3.2      spatial_7.3-14      pkgconfig_2.0.3    
##  [46] farver_2.1.1        nnet_7.3-16         sass_0.4.5         
##  [49] dbplyr_2.3.3        janitor_2.2.0       utf8_1.2.3         
##  [52] tidyselect_1.2.0    labeling_0.4.2      rlang_1.1.0        
##  [55] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [58] cachem_1.0.7        cli_3.6.1           generics_0.1.3     
##  [61] sjlabelled_1.2.0    broom_1.0.5         evaluate_0.20      
##  [64] fastmap_1.1.1       yaml_2.3.7          knitr_1.43         
##  [67] bit64_4.0.5         caTools_1.18.2      nlme_3.1-153       
##  [70] slam_0.1-50         xml2_1.3.3          tokenizers_0.3.0   
##  [73] compiler_4.1.2      rstudioapi_0.14     curl_5.0.0         
##  [76] bslib_0.4.2         highr_0.10          fBasics_4022.94    
##  [79] Matrix_1.6-0        its.analysis_1.6.0  nloptr_2.0.3       
##  [82] urca_1.3-3          vctrs_0.6.1         pillar_1.9.0       
##  [85] lifecycle_1.0.3     lmtest_0.9-40       jquerylib_0.1.4    
##  [88] estimability_1.4.1  bitops_1.0-7        insight_0.19.3     
##  [91] R6_2.5.1            KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [94] codetools_0.2-18    assertthat_0.2.1    boot_1.3-28        
##  [97] MASS_7.3-54         gtools_3.9.4        withr_2.5.0        
## [100] fracdiff_1.5-2      bayestestR_0.13.1   parallel_4.1.2     
## [103] hms_1.1.3           quadprog_1.5-8      timeDate_4022.108  
## [106] minqa_1.2.5         snakecase_0.11.0    rmarkdown_2.23     
## [109] carData_3.0-5       TTR_0.24.3          base64enc_0.1-3
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))